Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System

Abstract

When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms-such as perturb and observe (P&O) and hill-climbing (HC)-are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithmssuch as an artificial neural network (ANN) and fuzzy logic control (FLC)-have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB R is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.INDEX TERMS Adaptive jaya (Ajaya), maximum power point tracking (MPPT), metaheuristic algorithms, conventional algorithms, photovoltaic (PV).

Maximum power point tracking in a solarPVsystem: Current trends towards nature‐inspired optimization techniques

“…Otherwise, the newly updated solution will be generated. The flow chart of the Jaya algorithm applied to MPP tracking is shown in Figure 9, and its explanation is given below 47‐49 The population size ( n ) is chosen. Initial values of the candidate solution are chosen as fixed values. Fitness functions are evaluated. Convergence criteria are checked. Re‐initialization of the algorithm will take place in case of changing solar insolation. …”

“…Otherwise, the newly updated solution will be generated. The flow chart of the Jaya algorithm applied to MPP tracking is shown in Figure 9, and its explanation is given below [47][48][49] 1. The population size (n) is chosen.…”

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

“…By doing so, we reduce the number of update equations to one that avoid searching useless regions and reduce computational burden. Finally, we evaluated the LBNS against two recently proposed algorithms we mentioned before ( [16] and [17]), and the results showed that it significantly outperforms them. In addition, the proposed solution reduction technique can be applied to other metaheuristic techniques to further improve their performance in MPP tracking.…”

“…Due to this two-phase split, the global exploration and the convergence are improved compared to the simple Jaya algorithm. Moreover, in [17], another improved variant of the Jaya algorithm known as adaptive Jaya (AJaya) is proposed that uses varying iteration weighting coefficients in the Jaya update equations, improving the convergence speed of the simple Jaya algorithm and reducing the number of power fluctuations. Finally, in [18], a Most Valuable Player Algorithm (MVPA) algorithm uses a clever strategy based on the sensitivity of the Power versus Voltage and duty ratio to limit the search space to a set of solutions with a high probability of finding the global maximum there, and thus significantly improving the convergence speed and reducing the power losses.…”

Roach Infestation Optimization MPPT Algorithm for Solar Photovoltaic System

“…In this perspective, various artificial intelligent MPPT methodologies have been implemented to handle the shortcomings of the conventional MPPT methods, especially highly intermittent conditions. These include fuzzy logic control (FLC) [20], artificial neural network (ANN) [21], firefly algorithm (FA) [22], PSO [23], ant colony optimization (ACO) [24], flower pollination algorithm (FPA) [25], invasive weed optimization [26], salp swarm optimization [27], bat optimization [28], Neighboring-Pixel-based virtual imaging technique [29], surface-based polynomial fitting [30], Jaya algorithm [31], most valuable player algorithm [32] and many more. The results demonstrated that the artificial intelligence algorithms have high accuracy and stability in tracking the global MPP in different environmental conditions.…”